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from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
from langchain.llms import GooglePalm
# from langchain_community.llms import GooglePalm
from langchain import LLMChain, PromptTemplate
import gradio as gr
import numpy as np
import requests
import os
# Load image captioning model
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
def generate_caption_from_image(image_path):
# Process the image and generate caption
raw_image = Image.open(image_path).convert("RGB")
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
def generate_story_from_caption(caption):
# Generate story based on caption
api_key = os.getenv("GOOGLE_API")
prompt_template = """You are a story teller;
You can generate a short story based on a simple narrative, the story should between 30 to 80 words;
CONTEXT: {scenario}
Story: """
PROMPT = PromptTemplate(template=prompt_template, input_variables=["scenario"])
llm_chain = LLMChain(prompt=PROMPT,
llm=GooglePalm(google_api_key=api_key, temperature=0.8))
scenario = caption
story = llm_chain.run(scenario)
return story
def text_to_speech(text):
headers = {"Authorization": f"Bearer {os.getenv('HUGGING_FACE')}"}
payload = {"inputs": text}
API_URL = "https://api-inference.huggingface.co/models/facebook/mms-tts-eng"
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
with open("output.mp3", "wb") as f:
f.write(response.content)
return "output.mp3"
def generate_story_from_image(image_input):
input_image = Image.fromarray(image_input)
input_image.save("input_image.jpg")
image_path = 'input_image.jpg'
caption = generate_caption_from_image(image_path)
story = generate_story_from_caption(caption)
audio = text_to_speech(story)
return audio
# Define the input and output components
inputs = gr.Image(label="Image")
outputs = gr.Audio(label="Story Audio")
# Create the Gradio interface
gr.Interface(fn=generate_story_from_image, inputs=inputs, outputs=outputs, title="Story Teller").launch(debug=True) |